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声学感知刻度(mel scale、Bark scale、ERB)与声学特征提取(MFCC、BFCC、GFCC)_hz2erb-CSDN博客

声学感知刻度(mel scale、Bark scale、ERB)与声学特征提取(MFCC、BFCC、GFCC)

凌逆战

已于 2022-06-16 21:52:28 修改

阅读量5.1k

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文章标签：

python

开发语言

于 2022-05-28 19:42:00 首次发布

CSDN的所有文章均转载自我博客园的文章，由于存在转载丢失，想了解细节，可访问我的博客园。 https://www.cnblogs.com/LXP-Never/

本文链接：https://blog.csdn.net/qq_34218078/article/details/125145458

版权

本文地址：声学感知刻度(mel scale、Bark scale、ERB)与声学特征提取(MFCC、BFCC、GFCC) - 凌逆战 - 博客园 (引用请注明出处)

本文代码：GitHub - LXP-Never/perception_scale: Human ear perception scales and feature（mel、bark、ERB、gammatone）

作者：凌逆战 | Never.Ling

梅尔刻度

　　梅尔刻度(Mel scale)是一种由听众判断不同频率音高(pitch)彼此相等的感知刻度，表示人耳对等距音高(pitch)变化的感知。mel 刻度和正常频率(Hz)之间的参考点是将1 kHz，且高于人耳听阈值40分贝以上的基音，定为1000 mel。在大约500 Hz以上，听者判断越来越大的音程(interval)产生相等的pitch增量，人耳每感觉到等量的音高变化，所需要的频率变化随频率增加而愈来愈大。

　　将频率$f$ (Hz)转换为梅尔$m$的公式是：

$$m=2595\log_{10}(1+\frac{f}{700})$$

def hz2mel(hz):

""" Hz to Mels """

return 2595 * np.log10(1 + hz / 700.0)

mel与f(Hz)的对应关系

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

def hz2mel(hz):

""" Hz to Mels """

return 2595 * np.log10(1 + hz / 700.0)

if __name__ == "__main__":

fs = 16000

hz = np.linspace(0, 8000, 8000)

mel = hz2mel(hz)

fig = plt.figure()

ax = plt.plot(hz, mel, color="r")

plt.xlabel("Hertz scale (Hz)", fontsize=12) # x轴的名字

plt.ylabel("mel scale", fontsize=12)

plt.xticks(fontsize=10) # x轴的刻度

plt.yticks(fontsize=10)

plt.xlim(0, 8000) # 坐标轴的范围

plt.ylim(0)

def formatnum(x, pos):

return '$%.1f$' % (x / 1000)

formatter = FuncFormatter(formatnum)

# plt.gca().xaxis.set_major_formatter(formatter)

# plt.gca().yaxis.set_major_formatter(formatter)

plt.grid(linestyle='--')

plt.tight_layout()

plt.show()

画图代码

将梅尔$m$转换为频率$f$ (Hz)的公式是：

$$f=700e^{\frac{m}{2595}-1}$$

def mel2hz(mel):

""" Mels to HZ """

return 700 * (10 ** (mel / 2595.0) - 1)

mel 滤波器组

def mel_filterbanks(nfilt=20, nfft=512, samplerate=16000, lowfreq=0, highfreq=None):

"""计算一个Mel-filterbank (M,F)

:param nfilt: filterbank中的滤波器数量

:param nfft: FFT size

:param samplerate: 采样率

:param lowfreq: Mel-filter的最低频带边缘

:param highfreq: Mel-filter的最高频带边缘，默认samplerate/2

"""

highfreq = highfreq or samplerate / 2

# 按梅尔均匀间隔计算点

lowmel = hz2mel(lowfreq)

highmel = hz2mel(highfreq)

melpoints = np.linspace(lowmel, highmel, nfilt + 2)

hz_points = mel2hz(melpoints) # 将mel频率再转到hz频率

# bin = samplerate/2 / NFFT/2=sample_rate/NFFT # 每个频点的频率数

# bins = hz_points/bin=hz_points*NFFT/ sample_rate # hz_points对应第几个fft频点

bin = np.floor((nfft + 1) * hz_points / samplerate)

fbank = np.zeros([nfilt, int(nfft / 2 + 1)]) # (m,f)

for i in range(0, nfilt):

for j in range(int(bin[i]), int(bin[i + 1])):

fbank[i, j] = (j - bin[i]) / (bin[i + 1] - bin[i])

for j in range(int(bin[i + 1]), int(bin[i + 2])):

fbank[i, j] = (bin[i + 2] - j) / (bin[i + 2] - bin[i + 1])

# fbank -= (np.mean(fbank, axis=0) + 1e-8)

return fbank

mel 滤波器组特征

# -*- coding:utf-8 -*-

# Author:凌逆战 | Never

# Date: 2022/5/19

"""

1、提取Mel filterBank

2、提取mel spectrum

"""

import librosa

import numpy as np

import matplotlib.pyplot as plt

import librosa.display

from matplotlib.ticker import FuncFormatter

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False # 用来正常显示符号

def hz2mel(hz):

""" Hz to Mels """

return 2595 * np.log10(1 + hz / 700.0)

def mel2hz(mel):

""" Mels to HZ """

return 700 * (10 ** (mel / 2595.0) - 1)

def mel_filterbanks(nfilt=20, nfft=512, samplerate=16000, lowfreq=0, highfreq=None):

"""计算一个Mel-filterbank (M,F)

:param nfilt: filterbank中的滤波器数量

:param nfft: FFT size

:param samplerate: 采样率

:param lowfreq: Mel-filter的最低频带边缘

:param highfreq: Mel-filter的最高频带边缘，默认samplerate/2

"""

highfreq = highfreq or samplerate / 2

# 按梅尔均匀间隔计算点

lowmel = hz2mel(lowfreq)

highmel = hz2mel(highfreq)

melpoints = np.linspace(lowmel, highmel, nfilt + 2)

hz_points = mel2hz(melpoints) # 将mel频率再转到hz频率

# bin = samplerate/2 / NFFT/2=sample_rate/NFFT # 每个频点的频率数

# bins = hz_points/bin=hz_points*NFFT/ sample_rate # hz_points对应第几个fft频点

bin = np.floor((nfft + 1) * hz_points / samplerate)

fbank = np.zeros([nfilt, int(nfft / 2 + 1)]) # (m,f)

for i in range(0, nfilt):

for j in range(int(bin[i]), int(bin[i + 1])):

fbank[i, j] = (j - bin[i]) / (bin[i + 1] - bin[i])

for j in range(int(bin[i + 1]), int(bin[i + 2])):

fbank[i, j] = (bin[i + 2] - j) / (bin[i + 2] - bin[i + 1])

# fbank -= (np.mean(fbank, axis=0) + 1e-8)

return fbank

wav_path = "./p225_001.wav"

fs = 16000

NFFT = 512

win_length = 512

num_filter = 22

low_freq_mel = 0

high_freq_mel = hz2mel(fs // 2) # 求最高hz频率对应的mel频率

mel_points = np.linspace(low_freq_mel, high_freq_mel, num_filter + 2) # 在mel频率上均分成42个点

hz_points = mel2hz(mel_points) # 将mel频率再转到hz频率

print(hz_points)

# bin = sample_rate/2 / NFFT/2=sample_rate/NFFT # 每个频点的频率数

# bins = hz_points/bin=hz_points*NFFT/ sample_rate # hz_points对应第几个fft频点

bins = np.floor((NFFT + 1) * hz_points / fs)

print(bins)

# [ 0. 2. 5. 8. 12. 16. 20. 25. 31. 37. 44. 52. 61. 70.

# 81. 93. 107. 122. 138. 157. 178. 201. 227. 256.]

wav = librosa.load(wav_path, sr=fs)[0]

S = librosa.stft(wav, n_fft=NFFT, hop_length=NFFT // 2, win_length=win_length, window="hann", center=False)

mag = np.abs(S) # 幅度谱 (257, 127) librosa.magphase()

filterbanks = mel_filterbanks(nfilt=num_filter, nfft=NFFT, samplerate=fs, lowfreq=0, highfreq=fs // 2)

# ================ 画三角滤波器 ===========================

FFT_len = NFFT // 2 + 1

fs_bin = fs // 2 / (NFFT // 2) # 一个频点多少Hz

x = np.linspace(0, FFT_len, FFT_len)

plt.plot(x * fs_bin, filterbanks.T)

plt.xlim(0) # 坐标轴的范围

plt.ylim(0, 1)

plt.tight_layout()

plt.grid(linestyle='--')

plt.show()

filter_banks = np.dot(filterbanks, mag) # (M,F)*(F,T)=(M,T)

filter_banks = 20 * np.log10(filter_banks) # dB

# ================ 绘制语谱图 ==========================

# 绘制频谱图方法1

plt.imshow(filter_banks, cmap="jet", aspect='auto')

ax = plt.gca() # 获取其中某个坐标系

ax.invert_yaxis() # 将y轴反转

plt.tight_layout()

plt.show()

# 绘制频谱图方法2

plt.figure()

librosa.display.specshow(filter_banks, sr=fs, x_axis='time', y_axis='linear', cmap="jet")

plt.xlabel('时间/s', fontsize=14)

plt.ylabel('频率/kHz', fontsize=14)

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

def formatnum(x, pos):

return '$%d$' % (x / 1000)

formatter = FuncFormatter(formatnum)

plt.gca().yaxis.set_major_formatter(formatter)

plt.tight_layout()

plt.show()

画图代码

另外Librosa写好了完整的提取mel频谱和MFCC的API：

mel_spec = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=8000)

mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)

巴克刻度

　　巴克刻度(Bark scale)是于1961年由德国声学家Eberhard Zwicker提出的一种心理声学的尺度。它以Heinrich Barkhausen的名字命名，他提出了响度的第一个主观测量。[1]该术语的一个定义是“……等距离对应于感知上等距离的频率刻度”。高于约 500 Hz 时，此刻度或多或少等于对数频率轴。低于 500 Hz 时，Bark 标度变为越来越线性”。bark 刻度的范围是从1到24，并且它们与听觉的临界频带相对应。

频率f (Hz) 转换为 Bark：

$$\text { Bark }=13 \arctan (0.00076 f)+3.5 \arctan ((\frac{f}{7500})^{2})$$

Traunmüller, 1990 提出的新的Bark scale公式：

$$\operatorname{Bark}=\frac{26.81f}{1960+f}-0.53$$

反转：$f=\frac{1960((\operatorname{Bark}+0.53)-1)}{26.81}$

临界带宽(Hz)：$B_c=\frac{52548}{\operatorname{Bark}^2-52.56\operatorname{Bark}+690.39}$

Wang, Sekey & Gersho, 1992 提出了新的Bark scale公式：

$$\text { Bark }=6 \sinh ^{-1}(\frac{f}{600})$$

def hz2bark_1961(Hz):

return 13.0 * np.arctan(0.00076 * Hz) + 3.5 * np.arctan((Hz / 7500.0) ** 2)

def hz2bark_1990(Hz):

bark_scale = (26.81 * Hz) / (1960 + Hz) - 0.5

return bark_scale

def hz2bark_1992(Hz):

return 6 * np.arcsinh(Hz / 600)

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

def hz2bark_1961(Hz):

return 13.0 * np.arctan(0.00076 * Hz) + 3.5 * np.arctan((Hz / 7500.0) ** 2)

def hz2bark_1990(Hz):

bark_scale = (26.81 * Hz) / (1960 + Hz) - 0.5

return bark_scale

def hz2bark_1992(Hz):

return 6 * np.arcsinh(Hz / 600)

if __name__ == "__main__":

fs = 16000

hz = np.linspace(0, fs // 2, fs // 2)

bark_1961 = hz2bark_1961(hz)

bark_1990 = hz2bark_1990(hz)

bark_1992 = hz2bark_1992(hz)

plt.plot(hz, bark_1961, label="bark_1961")

plt.plot(hz, bark_1990, label="bark_1990")

plt.plot(hz, bark_1992, label="bark_1992")

plt.legend() # 显示图例

plt.xlabel("Hertz scale (Hz)", fontsize=12) # x轴的名字

plt.ylabel("Bark scale", fontsize=12)

plt.xticks(fontsize=10) # x轴的刻度

plt.yticks(fontsize=10)

plt.xlim(0, fs // 2) # 坐标轴的范围

plt.ylim(0)

def formatnum(x, pos):

return '$%.1f$' % (x / 1000)

formatter = FuncFormatter(formatnum)

# plt.gca().xaxis.set_major_formatter(formatter)

# plt.gca().yaxis.set_major_formatter(formatter)

plt.grid(linestyle='--')

plt.tight_layout()

plt.show()

画图代码

Bark 滤波器组

Bark频谱

import numpy as np

import librosa

import librosa.display

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False # 用来正常显示符号

def hz2bark(f):

""" Hz to bark频率 (Wang, Sekey & Gersho, 1992.) """

return 6. * np.arcsinh(f / 600.)

def bark2hz(fb):

""" Bark频率 to Hz """

return 600. * np.sinh(fb / 6.)

def fft2hz(fft, fs=16000, nfft=512):

""" FFT频点 to Hz """

return (fft * fs) / (nfft + 1)

def hz2fft(fb, fs=16000, nfft=512):

""" Bark频率 to FFT频点 """

return (nfft + 1) * fb / fs

def fft2bark(fft, fs=16000, nfft=512):

""" FFT频点 to Bark频率 """

return hz2bark((fft * fs) / (nfft + 1))

def bark2fft(fb, fs=16000, nfft=512):

""" Bark频率 to FFT频点 """

# bin = sample_rate/2 / nfft/2=sample_rate/nfft # 每个频点的频率数

# bins = hz_points/bin=hz_points*nfft/ sample_rate # hz_points对应第几个fft频点

return (nfft + 1) * bark2hz(fb) / fs

def Fm(fb, fc):

""" 计算一个特定的中心频率的Bark filter

:param fb: frequency in Bark.

:param fc: center frequency in Bark.

:return: 相关的Bark filter 值/幅度

"""

if fc - 2.5 <= fb <= fc - 0.5:

return 10 ** (2.5 * (fb - fc + 0.5))

elif fc - 0.5 < fb < fc + 0.5:

return 1

elif fc + 0.5 <= fb <= fc + 1.3:

return 10 ** (-2.5 * (fb - fc - 0.5))

else:

return 0

def bark_filter_banks(nfilts=20, nfft=512, fs=16000, low_freq=0, high_freq=None, scale="constant"):

""" 计算Bark-filterbanks,(B,F)

:param nfilts: 滤波器组中滤波器的数量 (Default 20)

:param nfft: FFT size.(Default is 512)

:param fs: 采样率，(Default 16000 Hz)

:param low_freq: MEL滤波器的最低带边。(Default 0 Hz)

:param high_freq: MEL滤波器的最高带边。(Default samplerate/2)

:param scale (str): 选择Max bins 幅度 "ascend"(上升)，"descend"(下降)或 "constant"(恒定)(=1)。默认是"constant"

:return:一个大小为(nfilts, nfft/2 + 1)的numpy数组，包含滤波器组。

"""

# init freqs

high_freq = high_freq or fs / 2

low_freq = low_freq or 0

# 按Bark scale 均匀间隔计算点数(点数以Bark为单位)

low_bark = hz2bark(low_freq)

high_bark = hz2bark(high_freq)

bark_points = np.linspace(low_bark, high_bark, nfilts + 4)

bins = np.floor(bark2fft(bark_points)) # Bark Scale等分布对应的 FFT bin number

# [ 0. 2. 5. 7. 10. 13. 16. 20. 24. 28. 33. 38. 44. 51.

# 59. 67. 77. 88. 101. 115. 132. 151. 172. 197. 224. 256.]

fbank = np.zeros([nfilts, nfft // 2 + 1])

# init scaler

if scale == "descendant" or scale == "constant":

c = 1

else:

c = 0

for i in range(0, nfilts): # --> B

# compute scaler

if scale == "descendant":

c -= 1 / nfilts

c = c * (c > 0) + 0 * (c < 0)

elif scale == "ascendant":

c += 1 / nfilts

c = c * (c < 1) + 1 * (c > 1)

for j in range(int(bins[i]), int(bins[i + 4])): # --> F

fc = bark_points[i+2] # 中心频率

fb = fft2bark(j) # Bark 频率

fbank[i, j] = c * Fm(fb, fc)

return np.abs(fbank)

if __name__ == "__main__":

nfilts = 22

NFFT = 512

fs = 16000

wav = librosa.load("p225_001.wav",sr=fs)[0]

S = librosa.stft(wav, n_fft=NFFT, hop_length=NFFT // 2, win_length=NFFT, window="hann", center=False)

mag = np.abs(S) # 幅度谱 (257, 127) librosa.magphase()

filterbanks = bark_filter_banks(nfilts=nfilts, nfft=NFFT, fs=fs, low_freq=0, high_freq=None, scale="constant")

# ================ 画三角滤波器 ===========================

FFT_len = NFFT // 2 + 1

fs_bin = fs // 2 / (NFFT // 2) # 一个频点多少Hz

x = np.linspace(0, FFT_len, FFT_len)

plt.plot(x * fs_bin, filterbanks.T)

# plt.xlim(0) # 坐标轴的范围

# plt.ylim(0, 1)

plt.tight_layout()

plt.grid(linestyle='--')

plt.show()

filter_banks = np.dot(filterbanks, mag) # (M,F)*(F,T)=(M,T)

filter_banks = 20 * np.log10(filter_banks) # dB

# ================ 绘制语谱图 ==========================

plt.figure()

librosa.display.specshow(filter_banks, sr=fs, x_axis='time', y_axis='linear', cmap="jet")

plt.xlabel('时间/s', fontsize=14)

plt.ylabel('频率/kHz', fontsize=14)

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

def formatnum(x, pos):

return '$%d$' % (x / 1000)

formatter = FuncFormatter(formatnum)

plt.gca().yaxis.set_major_formatter(formatter)

plt.tight_layout()

plt.show()

代码

等效矩阵带宽

　　等效矩形带宽(Equivalent Rectangular Bandwidth，ERB)是用于心理声学(研究人对声音(包括言语和音乐)的生理和心理反应的科学)的一种量度方法，它给出了一个近似于人耳听觉的对带宽的过滤方法，使用不现实但方便的简化方法将滤波器建模为矩形带通滤波器或带阻滤波器。

　　Moore 和 Glasberg在1983 年，对于中等的声强和年轻的听者，人的听觉滤波器的带宽可以通过以下的多项式方程式近似：

$$\operatorname{ERB}(f)=6.23 \cdot f^{2}+93.39 \cdot f+28.52$$

其中$f$为滤波器的中心频率(kHz)，$ERB(f)$为滤波器的带宽(Hz)。这个近似值是基于一些出版的同时掩蔽(Simultaneous masking)实验的结果。这个近似对于从0.1到6.5 kHz的范围是有效的。

　　它们也在1990年发表了另一(线性)近似：

$$\operatorname{ERB}(f)=24.7 *(4.37*10^{-3}*f+1)$$

其中$f$的单位是 Hz，$ERB(f)$的单位是 Hz。这个近似值适用于中等声级和0.1 到 10 kHz 之间的频率值。

1998发表了公式：

$$\operatorname{ERB}(f)=24.7 + \frac{f}{9.26449}$$

2002发表了公式：

\operatorname{ERB}(f)=9.265* \log(1 + \frac{f}{24.7* 9.265})

　　MATLAB的 VOICEBOX 语音处理工具箱的ERB公式：

$$\operatorname{ERBs}(f)=11.17268 \cdot \ln \left(1+\frac{46.06538 \cdot f}{f+14678.49}\right)$$

　　我看很多代码使用下面公式，但是下面公式和上面公式的

$$\operatorname{ERB}(f)=21.4 \cdot \log _{10}(1+ \frac{4.37\cdot f}{1000})$$

def hz2erb_1983(f):

""" 中心频率f(Hz) f to ERB(Hz) """

f = f / 1000.0

return (6.23 * (f ** 2)) + (93.39 * f) + 28.52

def hz2erb_1990(f):

""" 中心频率f(Hz) f to ERB(Hz) """

return 24.7 * (4.37 * f / 1000 + 1.0)

def hz2erb_1998(f):

""" 中心频率f(Hz) f to ERB(Hz)

hz2erb_1990 和 hz2erb_1990_2 的曲线几乎一模一样

M. Slaney, Auditory Toolbox, Version 2, Technical Report No: 1998-010, Internal Research Corporation, 1998

http://cobweb.ecn.purdue.edu/~malcolm/interval/1998-010/

"""

return 24.7 + (f / 9.26449)

def Hz2erb_2002(f):

""" [Hohmann2002] Equation 16 """

EarQ = 9.265 # _ERB_Q

minBW = 24.7 # minBW

return EarQ * np.log(1 + f / (minBW * EarQ))

def Hz2erb_matlab(f):

""" Convert Hz to ERB number """

n_erb = 11.17268 * np.log(1 + (46.06538 * f) / (f + 14678.49))

return n_erb

def Hz2erb_other(f):

""" Convert Hz to ERB number """

n_erb = 21.4 * np.log10(1 + 0.00437 * f)

return n_erb

其中erb_1990和erb_1998相差无几

erb202和Hz2erb_matlab和Hz2erb_other相差无几

线性滤波器组

使用ERB的线性滤波器组

# -*- coding:utf-8 -*-

# Author:凌逆战 | Never.Ling

# Date: 2022/5/28

"""

基于Josh McDermott的Matlab滤波器组代码:

https://github.com/wil-j-wil/py_bank

https://github.com/flavioeverardo/erb_bands

"""

import numpy as np

import librosa

import librosa.display

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False # 用来正常显示符号

class EquivalentRectangularBandwidth():

def __init__(self, nfreqs, sample_rate, total_erb_bands, low_freq, max_freq):

if low_freq == None:

low_freq = 20

if max_freq == None:

max_freq = sample_rate // 2

freqs = np.linspace(0, max_freq, nfreqs) # 每个STFT频点对应多少Hz

self.EarQ = 9.265 # _ERB_Q

self.minBW = 24.7 # minBW

# 在ERB刻度上建立均匀间隔

erb_low = self.freq2erb(low_freq) # 最低截止频率

erb_high = self.freq2erb(max_freq) # 最高截止频率

# 在ERB频率上均分为(total_erb_bands + )2个频带

erb_lims = np.linspace(erb_low, erb_high, total_erb_bands + 2)

cutoffs = self.erb2freq(erb_lims) # 将 ERB频率再转到 hz频率, 在线性频率Hz上找到ERB截止频率对应的频率

# self.nfreqs F

# self.freqs # 每个STFT频点对应多少Hz

self.filters = self.get_bands(total_erb_bands, nfreqs, freqs, cutoffs)

def freq2erb(self, frequency):

""" [Hohmann2002] Equation 16"""

return self.EarQ * np.log(1 + frequency / (self.minBW * self.EarQ))

def erb2freq(self, erb):

""" [Hohmann2002] Equation 17"""

return (np.exp(erb / self.EarQ) - 1) * self.minBW * self.EarQ

def get_bands(self, total_erb_bands, nfreqs, freqs, cutoffs):

"""

获取erb bands、索引、带宽和滤波器形状

:param erb_bands_num: ERB 频带数

:param nfreqs: 频点数 F

:param freqs: 每个STFT频点对应多少Hz

:param cutoffs: 中心频率 Hz

:param erb_points: ERB频带界限列表

:return:

"""

cos_filts = np.zeros([nfreqs, total_erb_bands]) # (F, ERB)

for i in range(total_erb_bands):

lower_cutoff = cutoffs[i] # 上限截止频率 Hz

higher_cutoff = cutoffs[i + 2] # 下限截止频率 Hz, 相邻filters重叠50%

lower_index = np.min(np.where(freqs > lower_cutoff)) # 下限截止频率对应的Hz索引 Hz。np.where 返回满足条件的索引

higher_index = np.max(np.where(freqs < higher_cutoff)) # 上限截止频率对应的Hz索引

avg = (self.freq2erb(lower_cutoff) + self.freq2erb(higher_cutoff)) / 2

rnge = self.freq2erb(higher_cutoff) - self.freq2erb(lower_cutoff)

cos_filts[lower_index:higher_index + 1, i] = np.cos(

(self.freq2erb(freqs[lower_index:higher_index + 1]) - avg) / rnge * np.pi) # 减均值，除方差

# 加入低通和高通，得到完美的重构

filters = np.zeros([nfreqs, total_erb_bands + 2]) # (F, ERB)

filters[:, 1:total_erb_bands + 1] = cos_filts

# 低通滤波器上升到第一个余cos filter的峰值

higher_index = np.max(np.where(freqs < cutoffs[1])) # 上限截止频率对应的Hz索引

filters[:higher_index + 1, 0] = np.sqrt(1 - np.power(filters[:higher_index + 1, 1], 2))

# 高通滤波器下降到最后一个cos filter的峰值

lower_index = np.min(np.where(freqs > cutoffs[total_erb_bands]))

filters[lower_index:nfreqs, total_erb_bands + 1] = np.sqrt(

1 - np.power(filters[lower_index:nfreqs, total_erb_bands], 2))

return cos_filts

if __name__ == "__main__":

fs = 16000

NFFT = 512 # 信号长度

ERB_num = 20

low_lim = 20 # 最低滤波器中心频率

high_lim = fs / 2 # 最高滤波器中心频率

freq_num = NFFT // 2 + 1

fs_bin = fs // 2 / (NFFT // 2) # 一个频点多少Hz

x = np.linspace(0, freq_num, freq_num)

# ================ 画三角滤波器 ===========================

ERB = EquivalentRectangularBandwidth(freq_num, fs, ERB_num, low_lim, high_lim)

filterbanks = ERB.filters.T # (257, 20)

plt.plot(x * fs_bin, filterbanks.T)

# plt.xlim(0) # 坐标轴的范围

# plt.ylim(0, 1)

plt.tight_layout()

plt.grid(linestyle='--')

plt.show()

# ================ 绘制语谱图 ==========================

wav = librosa.load("p225_001.wav", sr=fs)[0]

S = librosa.stft(wav, n_fft=NFFT, hop_length=NFFT // 2, win_length=NFFT, window="hann", center=False)

mag = np.abs(S) # 幅度谱 (257, 127) librosa.magphase()

filter_banks = np.dot(filterbanks, mag) # (M,F)*(F,T)=(M,T)

filter_banks = 20 * np.log10(filter_banks) # dB

plt.figure()

librosa.display.specshow(filter_banks, sr=fs, x_axis='time', y_axis='linear', cmap="jet")

plt.xlabel('时间/s', fontsize=14)

plt.ylabel('频率/kHz', fontsize=14)

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

def formatnum(x, pos):

return '$%d$' % (x / 1000)

formatter = FuncFormatter(formatnum)

plt.gca().yaxis.set_major_formatter(formatter)

plt.tight_layout()

plt.show()

View Code

Gammatone 滤波器组

　　外界语音信号进入耳蜗的基底膜后，将依据频率进行分解并产生行波震动，从而刺激听觉感受细胞。GammaTone 滤波器是一组用来模拟耳蜗频率分解特点的滤波器模型，由脉冲响应描述的线性滤波器，脉冲响应是gamma 分布和正弦(sin)音调的乘积。它是听觉系统中一种广泛使用的听觉滤波器模型。

历史

　　一般认为外周听觉系统的频率分析方式可以通过一组带通滤波器来进行一定程度的模拟，人们为此也提出了各种各样的滤波器组，如 roex 滤波器(Patterson and Moore 1986)。

在神经科学上有一种叫做反向相关性 “reverse correlation”(de Boer and Kuyper 1968)的计算方式，通过计算初级听觉神经纤维对于白噪声刺激的响应以及相关程度，即听觉神经元发放动作电位前的平均叠加信号，从而直接从生理状态上估计听觉滤波器的形状。这个滤波器是在外周听觉神经发放动作电位前生效的，因此得名为“revcor function”，可以作为一定限度下对外周听觉滤波器冲激响应的估计，也就是耳蜗等对音频信号的前置带通滤波。

　　1972年Johannesma提出了 gammatone 滤波器用来逼近recvor function：

$$时域表达式：g(t)=a t^{n-1} e^{-2 \pi b t} \cos (2 \pi f_c t+\phi_0)$$

其中$f_c(Hz)$是中心频率(center frequency)，$\phi_0$是初始相位(phase)，$a$是幅度(amplitude)，$n$是滤波器的阶数(order)，越大则偏度越低，滤波器越“瘦高”，$b(Hz)$是滤波器的3dB 带宽(bandwidth)，$t(s)$是时间。

这个时域脉冲响应是一个正弦曲线(pure tone)，其幅度包络是一个缩放的gamma分布函数。

我们可以通过时域表达式生成一组gammatone滤波器组和 gammatone滤波器组特征。

# -*- coding:utf-8 -*-

# Author:凌逆战 | Never.Ling

# Date: 2022/5/24

"""

时域滤波器组 FFT 转频域滤波器组与语音频谱相乘

参考：https://github.com/TAriasVergara/Acoustic_features

"""

import librosa

import librosa.display

import numpy as np

from scipy.fftpack import dct

import matplotlib.pyplot as plt

from matplotlib.ticker import FuncFormatter

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False # 用来正常显示符号

def erb_space(low_freq=50, high_freq=8000, n=64):

""" 计算中心频率(ERB scale)

:param min_freq: 中心频率域的最小频率。

:param max_freq: 中心频率域的最大频率。

:param nfilts: 滤波器的个数，即等于计算中心频率的个数。

:return: 一组中心频率

"""

ear_q = 9.26449

min_bw = 24.7

cf_array = -(ear_q * min_bw) + np.exp(

np.linspace(1, n, n) * (-np.log(high_freq + ear_q * min_bw) + np.log(low_freq + ear_q * min_bw)) / n) \

* (high_freq + ear_q * min_bw)

return cf_array

def gammatone_impulse_response(samplerate_hz, length_in_samples, center_freq_hz, p):

""" gammatone滤波器的时域公式

:param samplerate_hz: 采样率

:param length_in_samples: 信号长度

:param center_freq_hz: 中心频率

:param p: 滤波器阶数

:return: gammatone 脉冲响应

"""

# 生成一个gammatone filter (1990 Glasberg&Moore parametrized)

erb = 24.7 + (center_freq_hz / 9.26449) # equivalent rectangular bandwidth.

# 中心频率

an = (np.pi * np.math.factorial(2 * p - 2) * np.power(2, float(-(2 * p - 2)))) / np.square(np.math.factorial(p - 1))

b = erb / an # 带宽

a = 1 # 幅度(amplitude). 这在后面的归一化过程中有所不同。

t = np.linspace(1. / samplerate_hz, length_in_samples / samplerate_hz, length_in_samples)

gammatone_ir = a * np.power(t, p - 1) * np.exp(-2 * np.pi * b * t) * np.cos(2 * np.pi * center_freq_hz * t)

return gammatone_ir

def generate_filterbank(fs, fmax, L, N, p=4):

"""

L: 在样本中测量的信号的大小

N: 滤波器数量

p: Gammatone脉冲响应的阶数

"""

# 中心频率

if fs == 8000:

fmax = 4000

center_freqs = erb_space(50, fmax, N) # 中心频率列表

center_freqs = np.flip(center_freqs) # 反转数组

n_center_freqs = len(center_freqs) # 中心频率的数量

filterbank = np.zeros((N, L))

# 为每个中心频率生成滤波器

for i in range(n_center_freqs):

# aa = gammatone_impulse_response(fs, L, center_freqs[i], p)

filterbank[i, :] = gammatone_impulse_response(fs, L, center_freqs[i], p)

return filterbank

def gfcc(cochleagram, numcep=13):

feat = dct(cochleagram, type=2, axis=1, norm='ortho')[:, :numcep]

# feat-= (np.mean(feat, axis=0) + 1e-8)#Cepstral mean substration

return feat

def cochleagram(sig_spec, filterbank, nfft):

"""

:param sig_spec: 语音频谱

:param filterbank: 时域滤波器组

:param nfft: fft_size

:return:

"""

filterbank = powerspec(filterbank, nfft) # 时域滤波器组经过FFT变换

filterbank /= np.max(filterbank, axis=-1)[:, None] # Normalize filters

cochlea_spec = np.dot(sig_spec, filterbank.T) # 矩阵相乘

cochlea_spec = np.where(cochlea_spec == 0.0, np.finfo(float).eps, cochlea_spec) # 把0变成一个很小的数

# cochlea_spec= np.log(cochlea_spec)-np.mean(np.log(cochlea_spec),axis=0)

cochlea_spec = np.log(cochlea_spec)

return cochlea_spec, filterbank

def powerspec(X, nfft):

# Fourier transform

# Y = np.fft.rfft(X, n=n_padded)

Y = np.fft.fft(X, n=nfft)

Y = np.absolute(Y)

# non-redundant part

m = int(nfft / 2) + 1

Y = Y[:, :m]

return np.abs(Y) ** 2

if __name__ == "__main__":

nfilts = 22

NFFT = 512

fs = 16000

Order = 4

FFT_len = NFFT // 2 + 1

fs_bin = fs // 2 / (NFFT // 2) # 一个频点多少Hz

x = np.linspace(0, FFT_len, FFT_len)

# ================ 画三角滤波器 ===========================

# gammatone_impulse_response = gammatone_impulse_response(fs/2, 512, 200, Order) # gammatone冲击响应

generate_filterbank = generate_filterbank(fs, fs // 2, FFT_len, nfilts, Order)

filterbanks = powerspec(generate_filterbank, NFFT) # 时域滤波器组经过FFT变换

filterbanks /= np.max(filterbanks, axis=-1)[:, None] # Normalize filters

print(generate_filterbank.shape) # (22, 257)

# plt.plot(filterbanks.T)

plt.plot(x * fs_bin, filterbanks.T)

# plt.xlim(0) # 坐标轴的范围

# plt.ylim(0, 1)

plt.tight_layout()

plt.grid(linestyle='--')

plt.show()

# ================ 绘制语谱图 ==========================

wav = librosa.load("p225_001.wav", sr=fs)[0]

S = librosa.stft(wav, n_fft=NFFT, hop_length=NFFT // 2, win_length=NFFT, window="hann", center=False)

mag = np.abs(S) # 幅度谱 (257, 127) librosa.magphase()

filter_banks = np.dot(filterbanks, mag) # (M,F)*(F,T)=(M,T)

filter_banks = 20 * np.log10(filter_banks) # dB

plt.figure()

librosa.display.specshow(filter_banks, sr=fs, x_axis='time', y_axis='linear', cmap="jet")

plt.xlabel('时间/s', fontsize=14)

plt.ylabel('频率/kHz', fontsize=14)

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

def formatnum(x, pos):

return '$%d$' % (x / 1000)

formatter = FuncFormatter(formatnum)

plt.gca().yaxis.set_major_formatter(formatter)

plt.tight_layout()

plt.show()

View Code

　　可以看到低频段分得很细，高频段分得很粗，和人耳听觉特性较为符合。

$$频域表达式：\begin{aligned} H(f)=& a[R(f) \otimes S(f)] \\ =& \frac{a}{2}(n-1) !(2 \pi b)^{-n}\left\{e^{i \phi_0}\left[1+\frac{i(f-f_c)}{b} \right]^{-n}+e^{-i \phi_0}\left[1+\frac{i(f+f_c)}{b}\right]^{-n}\right\} \end{aligned}$$

频率表达式中$R(f)$是指数+阶跃函数的傅里叶变换，阶跃函数用来区别 t>0 和 t<0。$S(f)$是频率为$f_0$的余弦的傅里叶变换。可以看到是一个中心频率在$f_c$、在两侧按照e指数衰减的滤波器。通过上述表达式可以生成一组滤波器，求Gammatone滤波器组特征只需要将Gammatone滤波器组与语音幅度谱相乘即可得到Gammatone滤波器组特征。

# -*- coding:utf-8 -*-

# Author:凌逆战 | Never

# Date: 2022/5/24

"""

Gammatone-filter-banks implementation

based on https://github.com/mcusi/gammatonegram/

"""

import librosa

import librosa.display

import numpy as np

from matplotlib import pyplot as plt

from matplotlib.ticker import FuncFormatter

plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签

plt.rcParams['axes.unicode_minus'] = False # 用来正常显示符号

# Slaney's ERB Filter constants

EarQ = 9.26449

minBW = 24.7

def generate_center_frequencies(min_freq, max_freq, filter_nums):

""" 计算中心频率(ERB scale)

:param min_freq: 中心频率域的最小频率。

:param max_freq: 中心频率域的最大频率。

:param filter_nums: 滤波器的个数，即等于计算中心频率的个数。

:return: 一组中心频率

"""

# init vars

n = np.linspace(1, filter_nums, filter_nums)

c = EarQ * minBW

# 计算中心频率

cfreqs = (max_freq + c) * np.exp((n / filter_nums) * np.log(

(min_freq + c) / (max_freq + c))) - c

return cfreqs

def compute_gain(fcs, B, wT, T):

""" 为了阶数计算增益和矩阵化计算

:param fcs: 中心频率

:param B: 滤波器的带宽

:param wT: 对应于用于频域计算的 w * T = 2 * pi * freq * T

:param T: 周期(单位秒s)，1/fs

:return:

Gain: 表示filter gains 的2d numpy数组

A: 用于最终计算的二维数组

"""

# 为了简化预先计算

K = np.exp(B * T)

Cos = np.cos(2 * fcs * np.pi * T)

Sin = np.sin(2 * fcs * np.pi * T)

Smax = np.sqrt(3 + 2 ** (3 / 2))

Smin = np.sqrt(3 - 2 ** (3 / 2))

# 定义A矩阵的行

A11 = (Cos + Smax * Sin) / K

A12 = (Cos - Smax * Sin) / K

A13 = (Cos + Smin * Sin) / K

A14 = (Cos - Smin * Sin) / K

# 计算增益 (vectorized)

A = np.array([A11, A12, A13, A14])

Kj = np.exp(1j * wT)

Kjmat = np.array([Kj, Kj, Kj, Kj]).T

G = 2 * T * Kjmat * (A.T - Kjmat)

Coe = -2 / K ** 2 - 2 * Kj ** 2 + 2 * (1 + Kj ** 2) / K

Gain = np.abs(G[:, 0] * G[:, 1] * G[:, 2] * G[:, 3] * Coe ** -4)

return A, Gain

def gammatone_filter_banks(nfilts=22, nfft=512, fs=16000, low_freq=None, high_freq=None, scale="contsant", order=4):

""" 计算Gammatone-filterbanks, (G,F)

:param nfilts: filterbank中滤波器的数量 (Default 22)

:param nfft: FFT size (Default is 512)

:param fs: 采样率 (Default 16000 Hz)

:param low_freq: 最低频带 (Default 0 Hz)

:param high_freq: 最高频带 (Default samplerate/2)

:param scale: 选择Max bins 幅度 "ascend"(上升)，"descend"(下降)或 "constant"(恒定)(=1)。默认是"constant"

:param order: 滤波器阶数

:return: 一个大小为(nfilts, nfft/2 + 1)的numpy数组，包含滤波器组。

"""

# init freqs

high_freq = high_freq or fs / 2

low_freq = low_freq or 0

# define custom difference func

def Dif(u, a):

return u - a.reshape(nfilts, 1)

# init vars

fbank = np.zeros([nfilts, nfft])

width = 1.0

maxlen = nfft // 2 + 1

T = 1 / fs

n = 4

u = np.exp(1j * 2 * np.pi * np.array(range(nfft // 2 + 1)) / nfft)

idx = range(nfft // 2 + 1)

fcs = generate_center_frequencies(low_freq, high_freq, nfilts) # 计算中心频率，转换到ERB scale

ERB = width * ((fcs / EarQ) ** order + minBW ** order) ** (1 / order) # 计算带宽

B = 1.019 * 2 * np.pi * ERB

# compute input vars

wT = 2 * fcs * np.pi * T

pole = np.exp(1j * wT) / np.exp(B * T)

# compute gain and A matrix

A, Gain = compute_gain(fcs, B, wT, T)

# compute fbank

fbank[:, idx] = (

(T ** 4 / Gain.reshape(nfilts, 1)) *

np.abs(Dif(u, A[0]) * Dif(u, A[1]) * Dif(u, A[2]) * Dif(u, A[3])) *

np.abs(Dif(u, pole) * Dif(u, pole.conj())) ** (-n))

# 确保所有filters的最大值为1.0

try:

fbs = np.array([f / np.max(f) for f in fbank[:, range(maxlen)]])

except BaseException:

fbs = fbank[:, idx]

# compute scaler

if scale == "ascendant":

c = [

]

for i in range(1, nfilts):

x = c[i - 1] + 1 / nfilts

c.append(x * (x < 1) + 1 * (x > 1))

elif scale == "descendant":

c = [

]

for i in range(1, nfilts):

x = c[i - 1] - 1 / nfilts

c.append(x * (x > 0) + 0 * (x < 0))

else:

c = [1 for i in range(nfilts)]

# apply scaler

c = np.array(c).reshape(nfilts, 1)

fbs = c * np.abs(fbs)

return fbs

if __name__ == "__main__":

nfilts = 22

NFFT = 512

fs = 16000

FFT_len = NFFT // 2 + 1

fs_bin = fs // 2 / (NFFT // 2) # 一个频点多少Hz

x = np.linspace(0, FFT_len, FFT_len)

# ================ 画三角滤波器 ===========================

filterbanks = gammatone_filter_banks(nfilts=22, nfft=512, fs=16000,

low_freq=None, high_freq=None,

scale="contsant", order=4)

print(filterbanks.shape) # (22, 257)

plt.plot(x * fs_bin, filterbanks.T)

# plt.xlim(0) # 坐标轴的范围

# plt.ylim(0, 1)

plt.tight_layout()

plt.grid(linestyle='--')

plt.show()

# ================ 绘制语谱图 ==========================

wav = librosa.load("p225_001.wav", sr=fs)[0]

S = librosa.stft(wav, n_fft=NFFT, hop_length=NFFT // 2, win_length=NFFT, window="hann", center=False)

mag = np.abs(S) # 幅度谱 (257, 127) librosa.magphase()

filter_banks = np.dot(filterbanks, mag) # (M,F)*(F,T)=(M,T)

filter_banks = 20 * np.log10(filter_banks) # dB

plt.figure()

librosa.display.specshow(filter_banks, sr=fs, x_axis='time', y_axis='linear', cmap="jet")

plt.xlabel('时间/s', fontsize=14)

plt.ylabel('频率/kHz', fontsize=14)

plt.xticks(fontsize=14)

plt.yticks(fontsize=14)

def formatnum(x, pos):

return '$%d$' % (x / 1000)

formatter = FuncFormatter(formatnum)

plt.gca().yaxis.set_major_formatter(formatter)

plt.tight_layout()

plt.show()

View Code

　　1988年Holdsworth 等人进一步阐明了GTF的各种特性，而且提供了一个数字IIR滤波器设计方案。这个技术使得GTF能够比FIR更加容易且高效地实现，为后续出现一些重要的实际应用做了铺垫。听觉滤波的gammatone模型的变化和改进包括复数gammatone滤波器、gammachirp滤波器、全极点(all-pole)和一零(one-zero) gammatone滤波器、双边(two-sided)gammatone滤波器和滤波器级联(filter-cascade)模型，以及各种level相关和这些的动态非线性版本。

参考

【博客】Auditory scales of frequency representation

【百度百科】心理声学

【维基百科】Bark scale

【维基百科】Mel scale

【维基百科】Equivalent rectangular bandwidth

【维基百科】Gammatone filter(包含了C \ C++ \ mathematica \ matlab的代码实现)

【博客】Equivalent Rectangular Bandwidth

【CSDN】GammaTone 滤波器详解

【python库】PyFilterbank

【代码】Brookes, Mike (22 December 2012). "frq2erb". VOICEBOX: Speech Processing Toolbox for MATLAB. Department of Electrical & Electronic Engineering, Imperial College, UK. Retrieved 20 January 2013.

【代码】Brookes, Mike (22 December 2012). "erb2frq". VOICEBOX: Speech Processing Toolbox for MATLAB. Department of Electrical & Electronic Engineering, Imperial College, UK. Retrieved 20 January 2013.

【论文】Smith, Julius O.; Abel, Jonathan S. (10 May 2007). "Equivalent Rectangular Bandwidth". Bark and ERB Bilinear Transforms. Center for Computer Research in Music and Acoustics (CCRMA), Stanford University, USA. Retrieved 20 January 2013.

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在电力系统自动化中，频率与有功功率调节是一个重要的方面。频率是指电力系统中电压和功率波动的频率，是电能质量的重要指标之一。频率偏移是电力系统有功功率不平衡的结果，需要通过调频来实现功率平衡。频率调节可以在电源侧和负荷侧进行，分别通过调节机组的有功输出和切除或增加负荷来实现频率的稳定。

频率调节的意义和特点在于，频率调节可以影响整个电力系统的运行状态和电能质量，是一个系统级的自动控制过程。频率调节不仅需要考虑系统的稳定性和安全性，还需要与经济运行密切相关。因此，在电力系统自动化中，频率调节是一个复杂而重要的问题，需要综合考虑系统的各种运行参数和需求。

在电力系统频率与有功功率调节中，同步发电机组调速系统是一个基本的工作原理。调速系统通过监测发电机的转速和负载情况，调整机组的有功输出，以维持系统频率的稳定。同时，调速器的特性和工作原理也对频率调节起着重要的作用，需要确保调速器的响应速度和精度，以及与其他系统设备的协调性。

电力系统频率的波动规律与对策是另一个重要内容，需要根据不同时间尺度的频率波动，制定相应的调频调控策略。随机小波动可以通过调速器自动调节来实现一次调频；计划外负荷变化需要调整调速器的功率给定来实现二次调频；而长时间尺度的频率波动需要预测负荷并制定合理的发电计划来实现频率的稳定。

总的来说，电力系统自动化中的频率与有功功率调节涉及到多个方面，包括调速系统的基本原理和特性、频率调节的意义和特点、以及不同时间尺度下的频率波动规律与对策。通过科学合理地进行频率调节，可以提高电力系统的运行效率和可靠性，确保系统稳定运行。因此，电力系统自动化中的频率与有功功率调节是一个至关重要的环节，需要引起相关工程师和研究人员的高度重视和深入研究。管理建模和仿真的文件管理Boualem Benatallah引用此版本：布阿利姆·贝纳塔拉。管理建模和仿真。约瑟夫-傅立叶大学-格勒诺布尔第一大学，1996年。法语。NNT：电话：00345357HAL ID：电话：00345357https://theses.hal.science/tel-003453572008年12月9日提交HAL是一个多学科的开放存取档案馆，用于存放和传播科学研究论文，无论它们是否被公开。论文可以来自法国或国外的教学和研究机构，也可以来自公共或私人研究中心。L’archive ouverte pluridisciplinaire Revit API中的定制化界面开发 # 1. Revit API简介

## 1.1 什么是Revit API

Revit API是Autodesk公司提供的一组应用程序接口，旨在帮助开发人员扩展Revit软件的功能。通过Revit API，开发人员可以创建定制化的界面、实现自动化任务以及与Revit模型进行交互。

## 1.2 Revit API的优势和应用场景

Revit API的优势在于提供了丰富的功能和灵活性，使得开发人员能够根据特定需求定制化界面，提高工作效率。应用场景包括但不限于自动化流程、创建特定定制功能、与其他软件集成等。

## 1.3 Revit API与其他BIM工具的对比

与其他BIM工具相比，R java实现计算机随机出题：随机出十道小学四则运算题目，要求操作数为10以内，运算加、减、乘、除，并可以根据用户的答案给出“√”或者“×”提示，最后计算总得分。好的，以下是 Java 代码实现这个功能：

```java

import java.util.Random;

import java.util.Scanner;

public class RandomArithmeticQuiz {

public static void main(String[] args) {

Scanner scanner = new Scanner(System.in);

Random random = new Random();

int score = 0;

for (int i = 0; i 创意风格PPT模板 (36).pptx MOMODA PowerPoint模板展示了一种创意风格，设计简洁大方，展示内容清晰明了。每一页PPT模板都呈现出一种独特的风格，让演示内容更加生动有趣。Morbi dignissim nisl in diam sagittis, id dapibus nulla pretium. Sed vitae quam metus. Etiam fermentum turpis vel lectus dignissim consectetur. 这些PPT模板适合各种场合使用，无论是商务演示还是教育培训，都能够轻松展示您的内容。

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应用于噪声环境下语种识别的GFCC改进算法

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周大春, 邵玉斌, 张昊阁, 龙华, 彭艺. 应用于噪声环境下语种识别的GFCC改进算法[J]. 云南大学学报(自然科学版). doi: 10.7540/j.ynu.20220531

引用本文:

周大春, 邵玉斌, 张昊阁, 龙华, 彭艺. 应用于噪声环境下语种识别的GFCC改进算法[J]. 云南大学学报(自然科学版). doi: 10.7540/j.ynu.20220531

ZHOU Da-chun, SHAO Yu-bin, ZHANG Hao-ge, LONG Hua, PENG Yi. An improved GFCC algorithm for language recognition in noisy environments[J]. Journal of Yunnan University: Natural Sciences Edition. DOI: 10.7540/j.ynu.20220531

Citation:

应用于噪声环境下语种识别的GFCC改进算法

周大春,

邵玉斌,

张昊阁,

龙华,

彭艺

An improved GFCC algorithm for language recognition in noisy environments

ZHOU Da-chun,

SHAO Yu-bin,

ZHANG Hao-ge,

LONG Hua,

PENG Yi

摘要

HTML全文

图(5)

表(2)

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摘要

摘要:

不同的噪声在频谱上有不同的特点，使得自动语种识别的性能在噪声环境下显著下降. 针对该问题，提出一种基于改进时域伽马通滤波器倒谱系数(gammatone filter cepstral coefficient, GFCC)特征的语种识别方法. 首先，提取不同噪声背景下的训练集的时域GFCC特征；然后，利用Fisher比计算特征各维对区分语种的相对贡献度大小，分析不同噪声对时域GFCC特征各维的影响，并根据分析来设计合适的权值对特征各维加权，得到语种区分性更强的特征集；最后，利用高斯混合−通用背景模型作为基线系统进行语种识别，以测试所提方法性能. 实验结果表明，在单一噪声背景，信噪比为−5 dB，噪声源分别为粉红噪声、餐厅噪声的条件下，所提方法相比于传统时域GFCC特征方法的识别率分别提升了40.1、20.6个百分点，在其他噪声背景、信噪比下的识别率也有一定程度的提升.

Abstract:

Different noises have different characteristics in the frequency spectrum, which makes the performance of automatic language identification significantly degraded in the noisy environment. To address this problem, a language identification method based on improved time-domain gammatone filter cepstral coefficient (GFCC) features is proposed. First, the time-domain GFCC features are extracted from the training set with different noise backgrounds. Then, the Fisher ratio is used to calculate the relative contribution of each dimension of the features to distinguish languages, to analyse the effect of different noises on each dimension of the time-domain GFCC features, and to design suitable weights to weight each dimension of the features based on the analysis to obtain a feature set with better language discriminatory properties. Finally, a Gaussian mixture model-universal background model is used as the baseline system for language identification to test the performance of the proposed method. The experimental results show that under the conditions of single noise background, signal-to-noise ratio of −5 dB, and noise sources of pink noise and restaurant noise respectively, the identification rate of the proposed method is improved by 40.1 percentage points and 20.6 percentage points respectively compared with the traditional time-domain GFCC feature method, and the identification rate under other noise background and signal-to-noise ratio is also improved to some extent.

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Python音频特征提取（MFCC, IMFCC, GFCC, LFCC, PNCC ...） - 知乎

Python音频特征提取（MFCC, IMFCC, GFCC, LFCC, PNCC ...） - 知乎切换模式写文章登录/注册Python音频特征提取（MFCC, IMFCC, GFCC, LFCC, PNCC ...）Drifter?he=human&level=vegetable"--初入音频领域，往往需要学会音频特征提取之后再进一步展开更多的其他工作，然而学会音频处理需要语音信号处理的各种知识（傅里叶变换、DCT变换啊、小波变换啊。。。乱七八糟的我也不懂）但是python有很多第三方库封装好了很多函数使得人们提取更加简单，常见的库（librosa、numpy...）但是这些都还不够，因为我是一点都不懂 hahahha...这里介绍一个库（spafe，直接提取，并且各种特征提取都有jupyter notebook范例）spafe: 简化的Python音频功能提取spafe旨在简化音频中的特征提取。该库涵盖：MFCC，IMFCC，GFCC，LFCC，PNCC，PLP等。它还提供了各种滤波器组模块（Mel，Bark和Gammatone滤波器组）和其他频谱统计信息。Fbank、MFCC、BFCC、GFCC、LFCC、MSRCC、NGCC、PNCC、PSRCC特征提取范例（好多特征都没听过...）spafe库地址安装极其简单： pip install spafe提取各种特征的范例程序地址：这个项目值得更多的Star！！！以下是小补充：范例程序中的fs, sig = scipy.io.wavfile.read("./test.wav")部分音频可能会报错，换成下面这个会好点sig, fs = librosa.load("./test.wav", sr=16000)发布于 2020-04-01 22:33特征提取音频处理音频信息处理赞同 6024 条评论分享喜欢收藏申请

基于MFCC与GFCC混合特征参数的说话人识别

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应用科学学报 ›› 2019, Vol. 37 ›› Issue (1): 24-32.doi: 10.3969/j.issn.0255-8297.2019.01.003

• 信号与信息处理 •

基于MFCC与GFCC混合特征参数的说话人识别

周萍, 沈昊, 郑凯鹏

桂林电子科技大学电子工程与自动化学院, 广西桂林 541004

收稿日期:2018-02-01

修回日期:2018-04-25

出版日期:2019-01-31

发布日期:2019-01-31

作者简介:周萍,教授,研究方向:语音识别与智能控制,E-mail:940809266@qq.com

基金资助:国家自然科学基金（No.61462017）；广西自然科学基金（No.2014GXNSFAA118353）；广西自动检测技术与仪器重点实验室基金（No.YQ15110）资助

Speaker Recognition Based on Combination of MFCC and GFCC Feature Parameters

ZHOU Ping, SHEN Hao, ZHENG Kai-peng

College of Electric Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi Province, China

Received:2018-02-01

Revised:2018-04-25

Online:2019-01-31

Published:2019-01-31

PDF

261

可视化

摘要/Abstract

摘要：针对说话人识别中单一参数表征不够全面的特点，将抗噪性能一般的传统MFCC参数与鲁棒性更强的GFCC参数相互融合，并结合它们的动态特性构成一种新的混合参数.针对特征参数维数过高造成的冗余，研究了每种特征参数各分量与识别结果的关系，舍弃其中贡献较低的分量以实现特征参数降维的目的，并将混合参数应用于基于高斯混合模型的说话人识别系统.仿真实验表明，该混合特征参数具有更好的识别性能和抗噪性.

关键词:

Mel频率倒谱系数,

混合特征参数,

Gammatone滤波器,

说话人识别

Abstract: Aiming at the issue that single feature parameter of speaker recognition has the shortcoming of low representation ability, a set of mixture feature parameters is formed by combining the single poor anti-noise Mel frequency cepstral coefficients (MFCC) with more robust Gammatone frequency cepstral coefficients (GFCC) and their dynamic differential in this paper. Since the high dimension of the mixture feature parameters, the relationships of each dimension of different feature parameters and recognition results is studied, where dimensionality reduction on high dimensional features is implemented by discarding the dimensions with low contribution ratio. After that, the combination of feature parameters was applied to the speaker recognition system based on Gaussian mixture model. Experimental results show that the combination of parameters can better describe the speakers' feature and have better anti-noise capability.

Key words:

combination of feature parameters,

Mel frequency cepstral coefficients (MFCC),

speaker recognition,

Gammatone filter

中图分类号:

TN912.34

引用本文

周萍, 沈昊, 郑凯鹏. 基于MFCC与GFCC混合特征参数的说话人识别[J]. 应用科学学报, 2019, 37(1): 24-32.

ZHOU Ping, SHEN Hao, ZHENG Kai-peng. Speaker Recognition Based on Combination of MFCC and GFCC Feature Parameters[J]. Journal of Applied Sciences, 2019, 37(1): 24-32.

使用本文

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An urgent call to Frame the Future | by The GFCC | Competitive Edge

rgent call to Frame the Future | by The GFCC | Competitive EdgeOpen in appSign upSign inWriteSign upSign inAn urgent call to Frame the FuturePositioning innovation, sustainability, resilience, inclusiveness, and partnerships at the forefrontThe GFCC·FollowPublished inCompetitive Edge·3 min read·Jun 20, 2022--ListenShareBy Roberto AlvarezOne year ago, we started the Frame the Future Conversation Series to discuss how to weave innovation, sustainability, resilience, inclusiveness and partnerships into competitiveness strategies. Since then the world has changed significantly. The Russian invasion of Ukraine shattered the world order, threw Europe in turmoil, and injected complexity into an already challenging global economic landscape.Beyond the humanitarian tragedy, the war has several other effects. It placed additional stress on global supply chains, made energy security a top priority across nations, caused a steep increase in food prices, and contributed to a spike in inflation. It has also thrown Russia back into isolation. Above all, the war cast renewed questions about an already weakened international system.The current state of the world raises questions about our work at the GFCC. What is the role of a global multi-stakeholder organization in this context? More importantly, how relevant is the agenda launched in December 2021? Is the Frame the Future recommendations still relevant in face of the dramatic events and changes the world has experienced lately? Here is my take on these matters.The answer to the first question is clear in my mind. The war has highlighted the importance of the work of the GFCC. In a time of distress, there is a huge need for collaboration platforms that could bring together different countries, sectors, and perspectives. Our organization has a footprint in 33 nations and a unique multi-stakeholder setting. What defines the GFCC is not a sector, an industry, or a political view. But a shared belief in the power of global collaboration to advance national economies and a common desire to be exposed to other realities and work together.In 2021, leaders from 87 countries participated in GFCC activities, and our members and fellows worked together to develop the Frame the Future agenda that is reflected on the report Frame the Future: Guidelines and Recommendations for Future Competitiveness. Is that agenda still relevant? Undoubtfully.Here it is how to connect the current crisis to the priorities we outlined and why it is crucial to advance its implementation.§ Innovation: crises require innovation at speed, and the war calls for innovation in institutions at a global scale to resolve the current and prevent future conflicts.§ Sustainability: the conflict in Ukraine has pushed energy security to center stage, reinforcing the need to invest in local sustainable energy capacity.§ Resilience: the war is threatening food security for many and stressing global supply chains, creating uttermost urgency for economies, societies, and organizations to become more adaptable and resilient in the face of major disruption and disaster.§ Inclusiveness: the conflict shows the importance of digital infrastructure for populations and underlines the need to bring all segments of society into the innovation economy.§ Partnership: crisis response calls for partnerships and tearing down barriers that impede collaboration at all levels between public and private sector.The tragic war in Ukraine is part of an increasingly complex global mosaic and serves as an urgent call to Frame the Future. The agenda that the GFCC launched in December 2021, during the Global Innovation Summit, is more relevant than ever and should be a priority in the years to come.We urge all concerned global stakeholders to check the Frame the Future report and join the GFCC in advancing this relevant agenda. Global leaders have also shared their insights about the future of competitiveness and how to embed sustainability, resilience, inclusiveness, innovation, and partnerships into strategies in an exclusive thought paper series available herehttps://www.thegfcc.org/thought-pieces . I invite you to check these articles.----FollowWritten by The GFCC190 Followers·Editor for Competitive EdgeThe Global Federation of Competitiveness Councils. A network of leaders committed to accelerating global prosperity through fostering innovation ecosystems.FollowHelpStatusAboutCareersBlogPrivacyTermsText to speechTeams

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The Global Federation of Competitiveness Councils is a global network of leaders from competitiveness councils around the world.

The fundamental drivers of national competitiveness are being knitted together in networks that now underpin global economic growth. Innovation, sustainability and resilience—once the foundation for national competitiveness advantage—are now global platforms for prosperity. Acting globally is now a prerequisite to economic competitiveness nationally.

In addition to the goals of sharing best practices among councils and creating a network of global leaders committed to their national prosperity and the prosperity of the world, the Global Federation of Competitiveness Councils will develop and present each year a statement of shared principles for accelerating global competitiveness and prosperity.

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Do you have a story to tell? Is your organization leading the charge in sustainability, competitiveness, and groundbreaking initiatives?

The GFCC Stories - Innovation Pioneers platform is searching for its next wave of inspiring GFCC members! We want to celebrate YOUR achievements and share them with our extensive network.

Here's what GFCC Stories offers:

↳ Share Your Success: We'll craft a compelling story highlighting your organization's impactful work.

↳ Boost Your Visibility: Reach a global audience through targeted social media promotion and our exclusive network.

↳ Spark Collaboration: Inspire others and foster new connections that lead to exciting partnerships and initiatives.

Your story could be about:

★ A groundbreaking innovation

★ A unique and successful partnership

★ A project making a positive change in your community

Ready to share your story?

Fill out this form: https://lnkd.in/eUa-bbTr, or send an email to info@thegfcc.org

Let's celebrate your achievements and keep pushing the boundaries of innovation together!

Need Inspiration? Check Out These Success Stories:

↳ "Cross-sectoral Initiative in Northern Ireland Produces Biomethane to Achieve Decarbonization," featuring the Center for Competitiveness (CforC) - https://lnkd.in/dwvpmq3d

↳ "Harnessing AI to Bridge the Digital Divide in Eastern Europe," featuring Prof. Razvan Bologa from the Bucharest University of Economic Studies - https://lnkd.in/ehjQ2Vxu

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In a recent episode of HEDx, Deborah L. Wince-Smith, President & CEO of the US Council on Competitiveness, delivers a compelling message on the critical role of technological innovation in today's economies.

Drawing on historical analogies, Ms. Wince-Smith, who also leads the Universities Research Leadership Forum at the GFCC | The Global Federation of Competitiveness Councils, emphasizes the urgent need for global universities to drive technological transformation.

This is essential to equip future generations with the skills they'll need to thrive in a competitive and productive world.

Tune in to the episode and discover

Why Ms. Wince-Smith believes we've moved from the "Little House on the Prairie" to the "Cyber House on the Prairie."

How universities can spearhead this crucial technological shift.

Practical steps to ensure our economies remain competitive in the face of rapid change.

Don't miss this inspiring conversation! Click below to listen!

EP 107. Lessons from the Bronze Age for university productivity and competitiveness

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We're thrilled to announce that Mr. Hiro Nishiguchi has joined our network as a Distinguished Fellow.

Mr. Nishiguchi brings a wealth of experience working at the intersection of business and policy, having championed innovation and collaboration between corporations and startups in Japan and globally.

A long-time partner of the GFCC, he previously served as a board member during his tenure at the Japan Innovation Network.

Let's give Mr. Nishiguchi a warm welcome in the comments below!

Read all details of this announcement here: https://lnkd.in/eBJPNqpJ

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We have been a strong proponent for the hydrogen economy, recognizing its role in achieving a low-carbon future. Updates

In 2021, we published an interview with Prof. Parker and Dr. Yasushi Sekine, delving into the potential of hydrogen for a greener future and its impact on global competitiveness. You can find it here: https://lnkd.in/gn-HE2g

Now, we will host an Expert Session, exclusive for our Members & Fellows, to discuss the growth of the Hydrogen Economy, on March 14th.

This Expert Session will explore

▶ The rise of hydrogen as a clean and versatile energy carrier for transportation, industry, and power generation.

▶ The global landscape of the hydrogen economy, highlighting opportunities and challenges for different nations.

▶ Showcasing advancements in technology, policy, and investment through ongoing hydrogen projects worldwide.

▶ Insights into financing strategies for hydrogen ventures, including current policy and funding models.

Comment on this post and share your thoughts on the future of the hydrogen economy. Let's start the conversation!

#hydrogeneconomy #renewableenergy #sustainability #greenfuture

Will hydrogen power a net-zero future?

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Our Expert Sessions are opportunities for members to directly connect with on-the-ground experts from various countries. These sessions provide members with valuable information, insights, and even business chances. Help us with the question below.

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The Aston University is proud to announce the launch of the Aston Digital Futures Institute (ADFI), a new institute dedicated to developing innovative solutions to some of the world's most pressing challenges!

The ADFI brings together experts from a wide range of disciplines, including engineering, business, and computer science, to work collaboratively on projects that have a real-world impact.

The initiative's focus goes from using AI to improve healthcare to tackling climate change and social inequality, and it's led by Professor Aleks Subic, Vice-Chancellor and Chief Executive of Aston, and co-chair of the GFCC University and Research Leadership Forum.

You can read the full GFCC Story here: https://lnkd.in/eniX-AA2

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Crises are inevitable, but are they also catalysts for positive change?

The DITC, a global initiative exploring the intersection of crisis and innovation, is on a mission to answer that question.

Here's what we've been up to:

Documented key discussions from 2022, exploring historical moments where innovation thrived during adversity.

Welcomed two talented researchers: John E. Katsos and Ailun Gu, who delved deep into the world of crisis definitions and frameworks.

Published a white paper summarizing findings, along with three insightful case studies:

- The 9/11 Attacks

- The COVID-19 Pandemic

- The Armed Conflict in Ukraine

Ready to dive deeper? Head over to our page and access all our reports and case studies: https://lnkd.in/eAyN8hCF

#Crisis #Innovation #CrisisManagement

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Despite facing devastating losses, Ukraine has unveiled remarkable innovation in the face of conflict.

This case study spotlights key examples and explores how the nation can leverage this momentum for a brighter future.

Get the full story and actionable insights: https://ow.ly/uj2Y50QCBme

#Ukraine #Innovation #Resilience #Crisis

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Early recognition is vital in pandemic response. But are we truly prepared?

Dive into our report to discover innovative solutions and bridge the gap in crisis preparedness.

Download the report: https://ow.ly/zqvQ50QBBig

#Crisis #Innovation

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We are delighted to announce Dr. Hippolyte Fofack, Research Associate at Harvard University Center for African Studies, as our new Distinguished Fellow.

With over 20 years of experience in economic policy, development economics, and international finance, Dr. Fofack brings invaluable expertise to our organization.

Formerly at the African Export-Import Bank, Dr. Fofack's contributions to initiatives like the African Continental Free Trade Agreement (AfCFTA) highlight his commitment to advancing economic development in Africa and beyond.

Join us in welcoming Dr. Fofack, and read more about his career here: https://lnkd.in/eEk64x79

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